On the sources of global land surface hydrologic predictability

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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On the sources of global land surface hydrologic predictability

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-1987-2013 ◽

2013 ◽

Vol 10 (2) ◽

pp. 1987-2013 ◽

Cited By ~ 11

Author(s):

S. Shukla ◽

J. Sheffield ◽

E. F. Wood ◽

D. P. Lettenmaier

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Lead Time ◽

Snow Water Equivalent ◽

Forecast Skill ◽

Prediction Skill ◽

Streamflow Prediction ◽

Infiltration Capacity ◽

Forecast Period ◽

Hydrologic Prediction

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic prediction skill at seasonal lead times (i.e. 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs – primarily the state of initial soil moisture and snow) and seasonal climate forecast skill (FS). In this study we quantify the contributions of IHCs and FS to seasonal hydrologic prediction skill globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the Variable Infiltration Capacity (VIC) macroscale hydrology model, one based on Ensemble Streamflow Prediction (ESP) and another based on Reverse-ESP (rESP), both for a 47 yr reforecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts obtained from each experiment with a control simulation forced with observed atmospheric forcings over the reforecast period and estimate the ratio of Root Mean Square Error (RMSE) of both experiments for each forecast initialization date and lead time. We find that in general, the contributions of IHCs are greater than the contribution of FS over the Northern (Southern) Hemisphere during the forecast period starting in October and January (April and July). Over snow dominated regions in the Northern Hemisphere the IHCs dominate the CR forecast skill for up to 6 months lead time during the forecast period starting in April. Based on our findings we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

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Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

Earth System Science Data ◽

10.5194/essd-11-101-2019 ◽

2019 ◽

Vol 11 (1) ◽

pp. 101-110 ◽

Cited By ~ 1

Author(s):

James W. Roche ◽

Robert Rice ◽

Xiande Meng ◽

Daniel R. Cayan ◽

Michael D. Dettinger ◽

...

Keyword(s):

Soil Moisture ◽

Sierra Nevada ◽

Land Surface ◽

Forest Canopy ◽

Snow Water Equivalent ◽

Process Models ◽

Series Data ◽

Climate Data ◽

Data Set ◽

Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

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Climate, snow, and soil moisture data set for the Tuolumne and Merced River watersheds, California, USA

10.5194/essd-2018-102 ◽

2018 ◽

Author(s):

James W. Roche ◽

Robert Rice ◽

Xiande Meng ◽

Daniel R. Cayan ◽

Michael D. Dettinger ◽

...

Keyword(s):

Soil Moisture ◽

Sierra Nevada ◽

Land Surface ◽

Forest Canopy ◽

Snow Water Equivalent ◽

Process Models ◽

Series Data ◽

Climate Data ◽

Data Set ◽

Data Repositories

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) includes temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8) spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (level 0), data that are serially continuous with noise and non-physical points removed (level 1), and, where possible, data that are gap-filled using linear interpolation or regression with a nearby station record (level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow-depth and co-located soil-moisture measurements (2–6) from four locations spanning the rain-to-snow transition zone in the centre of the domain. Spatial data layers pertinent to snowpack modelling in this data set are basin polygons and 100-m resolution rasters of elevation, vegetation type, forest basal area, tree height, and forest canopy cover, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

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Seasonal Hydrologic Forecasting: Do Multimodel Ensemble Averages Always Yield Improvements in Forecast Skill?

Journal of Hydrometeorology ◽

10.1175/2010jhm1267.1 ◽

2010 ◽

Vol 11 (6) ◽

pp. 1358-1372 ◽

Cited By ~ 34

Author(s):

Theodore J. Bohn ◽

Mergia Y. Sonessa ◽

Dennis P. Lettenmaier

Keyword(s):

Bias Correction ◽

Land Surface ◽

Land Surface Model ◽

Winter Precipitation ◽

Forecast Skill ◽

Surface Model ◽

Streamflow Prediction ◽

Infiltration Capacity ◽

Common Input ◽

Model Average

Abstract Multimodel techniques have proven useful in improving forecast skill in many applications, including hydrology. Seasonal hydrologic forecasting in large basins represents a special case of hydrologic modeling, in which postprocessing techniques such as temporal aggregation and time-varying bias correction are often employed to improve forecast skill. To investigate the effects that these techniques have on the performance of multimodel averaging, the performance of three hydrological models [Variable Infiltration Capacity, Sacramento/Snow-17, and the Noah land surface model] and two multimodel averages [simple model average (SMA) and multiple linear regression (MLR) with monthly varying model weights] are examined in three snowmelt-dominated basins in the western United States. These evaluations were performed for both simulating and forecasting [using the Ensemble Streamflow Prediction (ESP) method] monthly discharge, with and without monthly bias corrections. The single best bias-corrected model outperformed the multimodel averages of raw models in both retrospective simulations and ensemble mean forecasts in terms of RMSE. Forming an MLR multimodel average from bias-corrected models added only slight improvements over the best bias-corrected model. Differences in performance among all bias-corrected models and multimodel averages were small. For ESP forecasts, both bias correction and multimodel averaging generally reduced the RMSE of the ESP ensemble means at lead times of up to 6 months in months when flow is dominated by snowmelt, with the reduction increasing as lead time decreased. The primary reason for this is that aggregating simulated streamflows from daily to monthly time scales increases model cross correlation, which in turn reduces the effectiveness of multimodel averaging in reducing those components of model error that bias correction cannot address. This effect may be stronger in snowmelt-dominated basins because the interannual variability of winter precipitation is a common input to all models. It was also found that both bias correcting and multimodel averaging using monthly varying parameters yielded much greater error reductions than methods using time-invariant parameters.

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Modeling the Effects of Lakes and Wetlands on the Water Balance of Arctic Environments

Journal of Hydrometeorology ◽

10.1175/2009jhm1084.1 ◽

2010 ◽

Vol 11 (2) ◽

pp. 276-295 ◽

Cited By ~ 86

Author(s):

Laura C. Bowling ◽

Dennis P. Lettenmaier

Keyword(s):

Land Surface ◽

Climate Models ◽

Snow Water Equivalent ◽

Temporal Distribution ◽

Substantial Improvement ◽

Infiltration Capacity ◽

Storage Effects ◽

Snow Water ◽

Lake Evaporation ◽

Interseasonal Variability

Abstract Lakes, ponds, and wetlands are common features in many low-gradient arctic watersheds. Storage of snowmelt runoff in lakes and wetlands exerts a strong influence on both the interannual and interseasonal variability of northern rivers. This influence is often not well represented in hydrology models and the land surface schemes used in climate models. In this paper, an algorithm to represent the evaporation and storage effects of lakes and wetlands within the Variable Infiltration Capacity (VIC) macroscale hydrology model is described. The model is evaluated with respect to its ability to represent water temperatures, net radiation, ice freeze–thaw, and runoff production for a variety of high-latitude locations. It is then used to investigate the influence of surface storage on the spatial and temporal distribution of water and energy fluxes for the Kuparuk and Putuligayuk Rivers, on the Alaskan arctic coastal plain. Inclusion of the lake and wetland algorithm results in a substantial improvement of the simulated streamflow hydrographs, as measured using the monthly Nash–Sutcliffe efficiency. Simulations of runoff from the Putuligayuk watershed indicate that up to 80% of snow meltwater goes into storage each year and does not contribute to streamflow. Approximately 46% of the variance in the volume of snowmelt entering storage can be explained by the year-to-year variation in maximum snow water equivalent and the lake storage deficit from the previous summer. The simulated summer lake storage deficit is much lower than the cumulative precipitation minus lake evaporation (−47 mm, on average) as a result of simulated recharge from the surrounding uplands.

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ERA-Interim/Land: a global land surface reanalysis data set

Hydrology and Earth System Sciences ◽

10.5194/hess-19-389-2015 ◽

2015 ◽

Vol 19 (1) ◽

pp. 389-407 ◽

Cited By ~ 304

Author(s):

G. Balsamo ◽

C. Albergel ◽

A. Beljaars ◽

S. Boussetta ◽

E. Brun ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Snow Water Equivalent ◽

Weather Prediction ◽

Reanalysis Data ◽

Set Covering ◽

Surface Model ◽

Data Set ◽

Time Frequency ◽

Global Land

Abstract. ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.

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Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

The Cryosphere ◽

10.5194/tc-13-3045-2019 ◽

2019 ◽

Vol 13 (11) ◽

pp. 3045-3059 ◽

Cited By ~ 3

Author(s):

Nick Rutter ◽

Melody J. Sandells ◽

Chris Derksen ◽

Joshua King ◽

Peter Toose ◽

...

Keyword(s):

Spatial Variability ◽

Layer Thickness ◽

Snow Water Equivalent ◽

Estimation Accuracy ◽

Snow Layer ◽

Data Set ◽

Snow Water ◽

Retrieval Errors ◽

Snow Microstructure ◽

The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

10.5194/essd-2021-160 ◽

2021 ◽

Author(s):

Vincent Vionnet ◽

Colleen Mortimer ◽

Mike Brady ◽

Louise Arnal ◽

Ross Brown

Keyword(s):

Newfoundland And Labrador ◽

Land Surface ◽

Snow Water Equivalent ◽

Water Security ◽

Monitoring And Evaluation ◽

Surface Models ◽

Snow Water ◽

The Government ◽

Different Sources

Abstract. In situ measurements of snow water equivalent (SWE) – the depth of water that would be produced if all the snow melted – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth and derived bulk snow density are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations, and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020 and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro Quebec were included. CanSWE includes over one million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734372 (Vionnet et al., 2021).

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Snow as a source of predictability in seasonal forecasts?

10.5194/egusphere-egu21-7331 ◽

2021 ◽

Author(s):

Danny Risto ◽

Bodo Ahrens ◽

Kristina Fröhlich

Keyword(s):

Latent Heat ◽

Land Surface ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

Heat Fluxes ◽

Seasonal Forecasting ◽

Seasonal Forecasts ◽

Predictive Skill ◽

Temperature Forecast ◽

Snow Water

<p>Besides the ocean, the land surface is a crucial component for predictability at (sub-)seasonal time scales. While the prediction of 2m temperature up to several months is possible for some maritime regions, continental regions lack predictive skill. Improved representation of the land surface in seasonal forecasting systems could help to close this gap. Snow cover fraction and snow water equivalent (SWE) are essential properties of the land surface. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture. First, we analyse the dynamical relationships between snow, 2m temperature and sensible/latent heat fluxes in reanalysis data in the northern hemisphere. Then we investigate whether these relationships are also present in operational seasonal forecast models provided by Copernicus Climate Change Service (C3S). First results show that the quality of the 2m temperature forecast over continental regions drops sharply after the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. Forecasted anomalies in sensible and latent heat fluxes of continental land surfaces show predictive skill during winter and spring only locally in some places, which reduces potential interactions between snow/land surface and the atmosphere in the models. The goal of this ongoing work is to assess the importance of snow initialisation and parameterisation for seasonal forecasting.</p>

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The AMSU-Based Hydrological Bundle Climate Data Record—Description and Comparison with Other Data Sets

Remote Sensing ◽

10.3390/rs10101640 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1640 ◽

Cited By ~ 3

Author(s):

Ralph Ferraro ◽

Brian Nelson ◽

Tom Smith ◽

Olivier Prat

Keyword(s):

Land Surface ◽

Snow Water Equivalent ◽

Precipitable Water ◽

Climate Data ◽

Sea Ice Concentration ◽

Data Set ◽

Data Record ◽

Microwave Imager ◽

Global Precipitation ◽

Climate Data Record

Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager—SSM/I; the Advanced Microwave Sounding Unit—AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer—AMSR; the Tropical Rainfall Measurement Mission Microwave Imager—TMI; the Global Precipitation Mission Microwave Imager—GMI). Here, the focus is on measurements from the AMSU-A, AMSU-B, and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998, with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19, and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a climate data record (CDR) that utilizes brightness temperatures from fundamental CDRs (FCDRs) to generate thematic CDRs (TCDRs). The TCDRs include total precipitable water (TPW), cloud liquid water (CLW), sea-ice concentration (SIC), land surface temperature (LST), land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDRs are shown to be in general good agreement with similar products from other sources, such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Due to the careful intercalibration of the FCDRs, little bias is found among the different TCDRs produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

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